Real-time determination of meter drift via loss qualification and quantification

ABSTRACT

In one aspect, data characterizing a fuel storage facility can be received from a sensor in operable communication with the fuel storage facility. An estimate of meter drift of a flow meter of a fuel dispenser in fluid communication with the fuel storage facility can be determined based on the received data. The estimate of meter drift can be determined based on at least one predictive model that predicts whether a calibration parameter characterizing a calibration of the flow meter has deviated from a predetermined flow meter calibration parameter. The estimate of meter drift can be provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/046,345, filed Jun. 30, 2020, and entitled “FUEL LEAKDETERMINATION VIA PREDICTIVE MODELING,” which is incorporated herein byreference in its entirety.

FIELD

Systems and methods are provided for the real-time determination ofmeter drift via loss quantification and qualification. Relatedapparatus, techniques, and articles are also described.

BACKGROUND

An aspect of fuel dispenser operation during a fueling transaction isthat the fuel dispenser can accurately provide a desired amount of fuelto a fueling station customer. The fuel dispenser may typically includea flow meter that is responsible for measuring the amount of fuelprovided to the fueling station customer during the fueling transaction.The flow meter is accurately calibrated, usually on an annual basis, andcertified as such by applicable regulatory agencies to ensure that thefuel dispenser accurately provides the desired amount of fuel to thefueling station customer.

At present, fueling stations rely on annual or periodic calibration of afuel dispenser flow meter to ensure that the accuracy of fuel dispensingis maintained, and assume that the calibration of a fuel dispenser flowmeter is relatively stable between calibrations. However, it is knownthat occasionally the calibration of the flow meter responsible forensuring the accurate dispensing of fuel may deviate from its properparameter (which is referred to as “meter drift”) in betweencalibration. When this occurs, the fuel dispenser does not accuratelydispense the desired amount of fuel, which can cause a loss either tothe fueling station customer or to the fueling station depending on thedirection of calibration drift from the proper calibration parameter.Additionally, this may also impact fueling station inventoryreconciliation, which may result in inaccurate estimates of leaks fromone or more fueling tanks located at a fueling station, environmentalcontamination, damaged reputation, and public health risks. In addition,this may result in a lack of compliance with applicable environmentalprotection laws, which could result in heavy penalties issued to thefueling station owner from applicable regulatory agencies. Althoughthese losses may be small, the magnitude of these impacts increases asthe time between the onset of the meter drift and the detection of themeter drift increases. And, at present, some conventional systems forthe monitoring of wetstock at fueling stations do not provide theability to readily detect losses associated with meter drift on a moreregular basis.

SUMMARY

Systems and methods are provided for the real-time determination ofmeter drift via loss quantification and qualification. Relatedapparatus, techniques, and articles are also described.

In one aspect, data characterizing a fuel storage facility can bereceived from a sensor in operable communication with the fuel storagefacility. An estimate of meter drift of a flow meter of a fuel dispenserin fluid communication with the fuel storage facility can be determinedbased on the received data. The estimate of meter drift can bedetermined based on at least one predictive model that predicts whethera calibration parameter characterizing a calibration of the flow meterhas deviated from a predetermined flow meter calibration parameter. Theestimate of meter drift can be provided.

One or more of the following features can be included in any feasiblecombination. For example, the at least one predictive model can includea predetermined calibration parameter for the fuel storage facility, aphysics model for the fuel storage facility, and an error modelindicative of at least one degree of error in the data. For example, ameter drift loss quantity prediction can be determined for the fuelstorage facility, and the determining of the meter drift loss quantityprediction can be based on the received data, the determined calibrationparameter for the fuel storage facility, the physics model, and anoptimization of the error model. For example, the sensor can include oneor more of a dipstick, an automated tank gauge, a fuel leak detectionsensor, a magnetostrictive probe, a point of sale device, a forecourtcontroller, a back office system, and/or a fuel dispenser. For example,the data characterizing the fuel storage facility can include one ormore of an indication of leakage and/or a rate of leakage per unit timeof fuel from the fuel storage facility to the surrounding environment,environmental parameters of the fuel storage facility, an amount of fueladded to the fuel storage facility as a result of a delivery of fuelfrom a fuel supplier to the fuel storage facility, and/or an amount offuel removed from the fuel storage facility as a result of a sale offuel to a customer. For example, the estimate of meter drift can beprovided to a graphical user interface of a display communicativelycoupled to the server, and the graphical user interface can beconfigured to present a visual characterization of the meter drift lossquantity prediction on the display. For example, the estimate of meterdrift can be provided to a graphical user interface of a displaycommunicatively coupled to the server, the graphical user interface canbe configured to present a visual characterization of the estimate ofmeter drift on the display. For example, the estimate of meter drift canbe determined at a repeatable time interval. For example, the physicsmodel can be a fluid balance model. For example, the determining can bebased on mathematical programing and can include maximizing orminimizing a function characterized by the physics model and by at leastvarying input values of the function, the input valves characterizingthe received data, and computing an output value of the function, theoutput value characterizing the estimate of meter drift.

In another aspect, a system is provided and can include at least onedata processor and memory storing instructions configured to cause theat least one data processor to perform operations described herein. Theoperations can include receiving, from a sensor in operablecommunication with a fuel storage facility, data characterizing the fuelstorage facility; determining, based on the received data, an estimateof meter drift of a flow meter of a fuel dispenser in fluidcommunication with the fuel storage facility, the determining furtherbased on at least one predictive model that predicts whether acalibration parameter characterizing a calibration of the flow meter hasdeviated from a predetermined flow meter calibration parameter; andproviding the estimate of meter drift.

One or more of the following features can be included in any feasiblecombination. For example, the at least one predictive model can includea predetermined calibration parameter for the fuel storage facility, aphysics model for the fuel storage facility, and an error modelindicative of at least one degree of error in the data. For example, theoperations can further include determining a meter drift loss quantityprediction for the fuel storage facility, and the determining of themeter drift loss quantity prediction can be based on the received data,the predetermined calibration parameter for the fuel storage facility,the physics model, and an optimization of the error model. For example,the sensor can include one or more of a dipstick, an automated tankgauge, a fuel leak detection sensor, a magnetostrictive probe, a pointof sale device, a forecourt controller, a back office system, and/or afuel dispenser. For example, the data characterizing the fuel storagefacility can include one or more of an indication of leakage and/or arate of leakage per unit time of fuel from the fuel storage facility tothe surrounding environment, environmental parameters of the fuelstorage facility, an amount of fuel added to the fuel storage facilityas a result of a delivery of fuel from a fuel supplier to the fuelstorage facility, and/or an amount of fuel removed from the fuel storagefacility as a result of a sale of fuel to a customer. For example, theestimate of meter drift can be provided to a graphical user interface ofa display communicatively coupled to the server, and the graphical userinterface can be configured to present a visual characterization of themeter drift loss quantity prediction on the display. For example, thephysics model can be a fluid balance model. For example, the determiningcan be based on mathematical programing and can include maximizing orminimizing a function characterized by the physics model and by at leastvarying input values of the function, the input valves characterizingthe received data, and computing an output value of the function, theoutput value characterizing the estimate of meter drift. For example,the operations can further include determining the estimate of meterdrift at a repeatable time interval.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, causes at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including aconnection over a network (e.g. the Internet, a wireless wide areanetwork, a local area network, a wide area network, a wired network, orthe like), via a direct connection between one or more of the multiplecomputing systems, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIG. 1 is a process flow diagram illustrating an example process of someimplementations of the current subject matter that can provide forreal-time determination of meter drift via loss quantification andqualification;

FIG. 2 is a schematic diagram of an exemplary system for implementingthe current subject matter, as shown and described herein;

FIG. 3 is a schematic diagram of a fueling station that is in operablecommunication with the system of FIG. 2; and

FIG. 4 is a flow diagram that demonstrates an exemplary process fordetermining an estimate of meter drift and a prediction of lossassociated with meter drift.

It should be understood that the above-referenced drawings are notnecessarily to scale, presenting a somewhat simplified representation ofvarious preferred features illustrative of the basic principles of thedisclosure. The specific design features of the present disclosure,including, for example, specific dimensions, orientations, locations,and shapes, will be determined in part by the particular intendedapplication and use environment.

DETAILED DESCRIPTION

An aspect of fuel dispenser operation during a fueling transaction isthat the fuel dispenser can accurately provide a desired amount of fuelto a fueling station customer. At present, fueling stations rely onannual or periodic calibration of a fuel dispenser flow meter to ensurethat the accuracy of fuel dispensing is maintained, and assume that thecalibration of a fuel dispenser flow meter is relatively stable betweencalibrations. However, it is known that occasionally the calibration ofthe flow meter responsible for ensuring the accurate dispensing of fuelmay deviate from its proper parameter (which is referred to as “meterdrift”) in between calibration. The current subject matter includes amethodology that, in some implementations, can detect a drift in thecalibration of a flow meter of a fuel dispenser (known as “meter drift”)based on wetstock inventory reconciliation data that is collected at afueling station on a daily basis. The methodology can accurately trackunderground storage tank volumetric calibrations, thermal expansion offluid due to variations in ambient conditions, seasonal effects,short-deliveries (or potential frauds), and short-sales (or leakage indispenser nozzles, theft). This methodology can include physics basedfluid balancing that uses modeling of inventory and predictive modelingtechniques to reduce sources of error in meter drift detection and toquantify the losses associated with meter drift.

Physics based fluid balancing can include predictingtemperature-adjusted starting and ending levels of fuel in the fuelstorage facility over a given period of time and accounting for sales offuel from the fuel storage facility and deliveries of fuel to the fuelstorage facility, and leakage from the fuel storage facility during thegiven period of time. By employing physics based fluid balancing andpredictive modeling techniques that account for sources of error anddiscrepancies that the physics based fluid balancing cannot account forby itself, some implementations of the methodology can provide aprediction of losses attributable to meter drift of a fuel dispenserflow meter in fluid communication with the fuel storage facility.

FIG. 1 is a process flow diagram illustrating an example process 100 ofsome implementations of the current subject matter that can provide forthe real-time determination of meter drift via loss quantification andqualification.

At 110, data characterizing a fuel storage facility can be received froma sensor that is in operable communication with the fuel storagefacility. The sensor can, in some implementations, be configured todetermine a level of fuel stored in the fuel storage facility, and thedata characterizing the storage facility can include the level of fuelstored in the fuel storage facility. In some implementations, the sensorcan be configured to measure a temperature of the fuel stored in thefuel storage facility, and the data characterizing the storage facilitycan include the temperature of the fuel stored in the fuel storagefacility.

In some implementations, the sensor can include a dipstick,magnetostrictive probe, and/or an automated tank gauge configured tomeasure the level of fuel stored in the fuel storage facility. In someimplementations, the sensor can include a fuel leak detection sensorconfigured to determine whether the fuel storage facility is leakingfuel to the environment surrounding the fuel storage facility, and thedata characterizing the fuel storage facility can include one or more ofan indication of leakage and/or a rate of leakage per unit time of fuelfrom the fuel storage facility to the surrounding environment. In someimplementations, the sensor can include any one of a point of saledevice, a forecourt controller, a back office system, and a fueldispenser, each of which can be in operable communication with the fuelstorage facility and can be configured to record environmentalparameters of the fuel storage facility (e.g., ambient temperature,etc.) and the fuel stored therein (e.g., temperature, fuel level, etc.),and the data characterizing the fuel storage facility can include theenvironmental parameters of the fuel storage facility (e.g., ambienttemperature, etc.) and the fuel stored therein (e.g., temperature, fuellevel, etc.). In some implementations, the sensor can be configured todetermine an amount of fuel added to the fuel storage facility as aresult of a delivery of fuel from a fuel supplier to the fuel storagefacility, and the data characterizing the fuel storage facility caninclude amount of fuel added to the fuel storage facility as a result ofa delivery of fuel from a fuel supplier to the fuel storage facility. Insome implementations, the sensor can be configured to determine anamount of fuel removed from the fuel storage facility as a result of asale of fuel to a customer, and the data characterizing the fuel storagefacility can include the amount of fuel removed from the fuel storagefacility as a result of a sale of fuel to the customer. In someimplementations, the sensor can comprise a plurality of sensors thatincorporate one or more aspects of the functionality described above.

In some implementations, the fuel storage facility can be an undergroundfuel storage tank at a fueling station that is configured to supply fueldispensers at the fueling station with fuel. In some implementations,the fuel storage facility can comprise a plurality of underground fuelstorage tanks, each located at the fueling station, having one or moreof the aforementioned sensors in operable communication therewith, andconfigured to supply fuel dispensers at the fueling station with fuel.In some implementations, the fuel storage facility can be located at aseparate location from the fueling station.

In some implementations, the data can be received at a server. In someimplementations, the server can include a wetstock management servercommunicatively coupled to the plurality of sensors that can collect thedata. The server can be a remote, e.g., cloud-based, server located awayfrom the fuel storage facility and/or the fueling station, however insome implementations the server can be located at the fuel storagefacility and/or the fueling station. In some embodiments, the datareceived from the one or more of the plurality of sensors can becollected by an intermediary data collection device (not shown), such asan internet of things (IoT) or edge device, located on-site, and thedata collection device can transmit the collected data to the server forprocessing.

In some implementations, the data received from the sensor cancharacterize one or more aspects of the fuel storage facility for adesignated period of time (e.g., a day). For example, in someimplementations, the data can characterize an amount of fuel present inthe fuel storage facility at a start time of the designated period oftime, an amount of fuel added to the fuel storage facility by thedelivery of fuel from a fuel supplier, an amount of fuel removed fromthe fuel storage facility by the sale of fuel to a customer, an amountof fuel present in the fuel storage facility at an end time of thedesignated period of time, a capacity of the fuel storage facility, atype of fuel stored in the fuel storage facility, a grade of fuel storedin the fuel storage facility, ambient weather, temperature, and/orpressure conditions at the fuel storage facility, and a type of sensordisposed at the fuel storage facility. In some implementations, when thefuel storage facility comprises a plurality of fuel tanks, the data cancharacterize whether the plurality of fuel tanks are in fluidcommunication with some or all of each other, and the number of fueltanks that are in fluid communication with one another.

At 120, an estimate of meter drift of a flow meter of a fuel dispenserin fluid communication with the fuel storage facility can be determinedbased on the received data. The estimate of meter drift can bedetermined based on at least one predictive model that predicts whethera calibration parameter characterizing a calibration of the flow meterhas deviated from a predetermined flow meter calibration parameter. Insome implementations, the at least one predictive model can include apredetermined calibration parameter for the fuel storage facility, aphysics model for the fuel storage facility, and an error modelindicative of at least one degree of error in the data.

In some implementations, the predetermined calibration parameter caninclude one or more characteristics of the fuel storage facility, suchas a tank chart. In some implementations, when the predeterminedcalibration parameter is non-linear in nature (such as when thepredetermined calibration parameter is a tank chart), the predeterminedcalibration parameter can be approximated, for use by the at least onepredictive model, as a piece-wise linear function having a plurality ofpredetermined breakpoints, and a slope between each of the predeterminedbreakpoints can be determined by optimization of the error model.However, in some implementations, the predetermined calibrationparameter can be approximated using other techniques known to persons ofskill in the art. In some implementations, a number of the predeterminedbreakpoints can also be determined by optimization of the error model.In some implementations, the number of predetermined breakpoints can bedetermined using machine learning techniques that involve, for example,k-means and gradient boosted trees. In some implementations, thepredetermined calibration parameters can include, use, or be based on,data characterizing the fuel storage facility that has been previouslyobtained.

In some implementations, the physics model can include a fluid balancemodel that determines a predicted fuel level for the fuel storagefacility based on the received data, which can be used to derive anestimate of meter drift of a flow meter of a fuel dispenser in fluidcommunication with the fuel storage facility. For example, in someimplementations, the fluid balance model can predict a starting level offuel in the fuel storage facility for a given time period based on thestarting level of fuel in the fuel storage facility at the beginning ofa previous time period, the ending level of fuel in the fuel storagefacility at the conclusion of the previous time period, the amount offuel sold from the fuel storage facility during the previous timeperiod, the amount of fuel delivered to the fuel storage facility duringthe previous time period, and the amount of fuel that has leaked fromthe fuel storage facility into the surrounding environment.

In some implementations, the at least one predictive model can accountfor various errors and discrepancies between the starting level of fuelin the fuel storage facility for a given period of time (e.g., a day)and the ending level of fuel in the fuel storage facility for the givenperiod of time that cannot otherwise be accounted for by sales of fuelfrom the fuel storage facility during the given period of time anddeliveries of fuel from the fuel storage facility during the givenperiod of time. In some implementations, such errors and discrepanciescan be the result of meter drift of a flow meter of a fuel dispenser influid communication with the fuel storage facility. In someimplementations, such errors and discrepancies can be the result of oneor more of a leakage of fuel from the fuel storage facility during thegiven period of time, discrepancies in fuel sales from the fuel storagefacility and in fuel deliveries to the fuel storage facility resultingfrom calculation/measurement errors or theft of fuel, and the like.

In some implementations, the at least one predictive model can accountfor the errors and discrepancies for a series of periods of time (e.g.,a series of days). In some implementations, the at least one predictivemodel can account for the aforementioned errors and discrepancies by theuse of an error model. The error model can include one or more optimizerfunctions that can be used in conjunction with the physics model by theat least one predictive model to minimize various error correction termsfor use in determining an estimate of meter drift with a high degree ofaccuracy. For example, in some implementations, the error model canminimize a deviation, from 1, of an average correction factor for anymultiplicative errors introduced in determining the amount of fuel salesfrom the fuel storage facility on a given day, and a deviation, from 1,of an average correction factor for any additive errors introduced indetermining the amount of fuel delivered on the given day. In someimplementations, the error model can minimize a deviation, from 0, of anaverage leakage value for the fuel storage facility. In someimplementations, the error model can minimize a weighted average of eachof these deviations and further include error contributions associatedwith additive correction factors.

In some implementations, the error model can solve one or more linearequations using the minimized cost function and thereby determine theestimate of meter drift for the time period under consideration. In someimplementations, the determination of the estimate of meter drift can befurther based on mathematical programing and can include maximizing orminimizing a function characterized by the physics model and by at leastvarying input values of the function that characterizes the receiveddata, and computing an output value of the function that characterizesthe estimate of meter drift. In some implementations, the estimate ofmeter drift can be determined at one or more repeatable time intervals.

In some implementations, the at least one predictive model can receiveuser-provided parameters for use in determining the estimate of meterdrift. In some implementations, the user-provided parameters can includevarious data quality parameters which can be used by the at least onepredictive model to improve the quality of the received data that isused for determining the estimate of meter drift. For example, the dataquality parameters can include indications to ignore or remove portionsof the data if the predicted estimate of meter drift, as determined bythe at least one predictive model, exceeds a certain value. In addition,in some implementations, the data quality parameters can include anartificially-induced parameter that can be used by the at least onepredictive model as an accuracy benchmark against the predicted estimateof meter drift determined by the at least one predictive model.

In some implementations, the user-provided parameters can also includeerror model parameters that can influence the operating characteristicsof the error model. For example, in some implementations, the errormodel parameters can include the fuel delivery error correction weightterm, the predicted fuel leakage rate weight term, and/or upper/lowerbounds for aspects of the predetermined calibration parameter.

Below is an exemplary mathematical implementation of the predictivemodel described herein that incorporates the aforementionedpredetermined calibration parameter, physics model, and error model.Equation 1 describes a generalized fluid balance equation that can beused by the predictive model in some implementations of the currentsubject matter that incorporates a predetermined calibration parameterand a physics model:

Σ_(k=0) ^(j′(i+1)−1)({circumflex over (l)} _(k)*(b _(k+1) −b_(k)))+{circumflex over (l)} _(j′(i+1))*(EL_(i)*(1−coeff*({circumflexover (T)} _(i) −{circumflex over (T)} _(m)))−b _(j′(i+1)))=Σ_(k=0)^(j′(i)−1)({circumflex over (l)} _(k)*(b _(k+1) −b _(k)))+{circumflexover (l)} _(j′(i))*(SL_(i)*(1−coeff*({circumflex over (T)} _(i)−{circumflex over (T)} _(m)))−b _(j′(i)))−δS _(i)+ε_(i) +D_(i)−{circumflex over (λ)}_(i)  Eq. (1)

The parameters and expressions included in Equation (1) are evaluatedfor a given time window of interest w, and for a number of data points dunder consideration in the time window w. SL_(i) is the starting levelof fuel in the fuel storage facility at a particular instant i, ∀i∈{1, .. . d}, EL_(i) is the ending level of fuel in the fuel storage facilityat the instant i, ∀i∈{1, . . . d}, S_(i) is the volume of fuel removedfrom the fuel storage facility due to sales at the instant i, ∀i∈{1, . .. d}, D_(i) is the volume of fuel added to the fuel storage facility dueto deliveries at the instant i, ∀i∈{1, . . . , d}, {circumflex over(T)}_(i) is the temperature on day i, ∀i∈{1, . . . , d}, and {circumflexover (T)}_(m) is the median temperature for the time window w, and coeffis the coefficient of thermal expansion for the fuel.

Each of these aforementioned parameters are known, however, there aresome parameters which are not known and can be approximated via use ofan optimization model. These parameters include {circumflex over (δ)},which is the correction factor for the multiplicative error in salesmeasurement, {circumflex over (ε)}_(i), which is the correction factorfor the additive error deliveries measurement at an instant i, ∀i∈{1, .. . , d} (note that ∈_(i) is zero for all non-delivery data points), and{circumflex over (λ)}_(i), which is the leakage at an instant i, ∀i∈{1,. . . , d}, which can be either positive or negative depending onwhether there is a loss or gain to the fuel storage facility.

As the tank chart for the fuel storage facility is non-linear, theparameters of the tank chart can be approximated with a piece-wiselinear function. b_(j), ∀j∈{0, 1, . . . , n} is a set of n=bkspredetermined breakpoints in the piecewise linear correction to thestock level. In particular, b₁=0 and b_(n)=full height of the fuelstorage facility. j′(i)∈{0, 1, . . . , n−1} can be defined such thatb_(j′(i)) is the largest breakpoint that is less than or equal toSL_(i). {circumflex over (l)}_(j), ∀j∈{0, 1, . . . n−1} is the slope ofthe linear piece between b_(j) and b_(j+1), which is to be determined bythe optimization model.

In executing the optimization model, it is assumed that the correctionterm {circumflex over (δ)} should be close to 1. As such, a firstobjective of the optimization model is to minimize the difference of thecorrection term from 1. It is also assumed that the average leakagevalue

$\hat{\lambda} = {\frac{1}{d}{\sum\limits_{i = 1}^{d}\;{\hat{\lambda}}_{i}}}$

and additive corrective term

$\hat{ɛ} = {\frac{1}{d}{\sum\limits_{i = 1}^{d}\;{\hat{ɛ}}_{i}}}$

should be close to 0. As such, a second objective of the optimizationmodel is to minimize its differences for these terms from 0. In someimplementations, the optimization model can minimize a weighted averageof the two objectives.

Equation 2, provided below, shows a mathematical representation of anerror model that can be used by the predictive model in someimplementations of the current subject matter to optimize the physicsmodel and thereby provide an accurate estimate of meter drift. Foroptimizing the current problem, a concrete mathematical model fromlinear programming is used as the mathematical model can be directlydefined with the real-time data values supplied at the time of the modeldefinition.

c ^(li)Σ_(i=1) ^(d) x ^(l) _(i) +c ^({circumflex over (ε)}) x^({circumflex over (ε)}) +c ^(εi)Σ_(i=1) ^(d) x ^(ε) _(i) +c ^(δ) x ^(δ)+c ^({circumflex over (λ)}) x ^({circumflex over (λ)}) +c^({circumflex over (λ)}i)Σ_(i=1) ^(d) x ^(λ) _(i)  (2)

Equation (2) is an optimization function featuring various optimizerweights which can be adjusted depending on various data qualityparameters. The optimizer weights are defined as:

${c^{li} = \frac{1}{bks}},{c^{\hat{ɛ}} = {\left( \frac{Nd}{d} \right)*{eps}}},{c^{ɛ\; i} = {\left( \frac{Nd}{d} \right)*{eps}}},{c^{\hat{\lambda}} = {lamb}},{c^{\hat{\lambda}i} = {lamb}},{c^{\delta} = {\left( \frac{Ns}{d} \right)*\frac{1}{bks}}}$

Such that:

$\begin{matrix}{\mspace{76mu}{{{\hat{\epsilon} - 0} \leq x^{\epsilon}},{{0 - \hat{\epsilon}} \leq x^{\epsilon}}}} & (3) \\{\mspace{76mu}{{{\hat{\delta} - 1} \leq x^{\delta}},{{1 - \hat{\delta}} \leq x^{\delta}}}} & (4) \\{{{\hat{\lambda} - 0} \leq x^{\lambda}},{{{0 - \hat{\lambda}} \leq {{x^{\lambda}{\sum\limits_{k = 0}^{{j^{\prime}{({i + 1})}} - 1}\;\left( {{\hat{l}}_{k}*\left( {b_{k + 1} - b_{k}} \right)} \right)}} + {{\hat{l}}_{j^{\prime}{({i + 1})}}*\left( {{{EL}_{i}*\left( {1 - {{coeff}*\left( {{\hat{T}}_{i} - {\hat{T}}_{m}} \right)}} \right)} - b_{j^{\prime}{({i + 1})}}} \right)}}} = {{\sum\limits_{k = 0}^{{j^{\prime}{(i)}} - 1}\;\left( {{\hat{l}}_{k}*\left( {b_{k + 1} - b_{k}} \right)} \right)} + {{\hat{l}}_{j^{\prime}{(i)}}*\left( {{{SL}_{i}*\left( {1 - {{coeff}*\left( {{\hat{T}}_{i} - {\hat{T}}_{m}} \right)}} \right)} - b_{j^{\prime}{(i)}}} \right)} - {\hat{\delta}S_{i}} + {\hat{ɛ}}_{i} + D_{i} - {{\hat{\lambda}}_{i}\mspace{14mu}{\forall{i \in {\left\{ {1,\ldots\;,d} \right\}\mspace{14mu}\left( {{same}\mspace{14mu}{as}\mspace{14mu}{Equation}\mspace{14mu}(1)\mspace{14mu}{above}} \right)}}}}}}} & (6) \\{\mspace{76mu}{\hat{\lambda} = {\frac{1}{d}{\sum\limits_{i = 1}^{d}\;{\hat{\lambda}}_{i}}}}} & (7) \\{\mspace{76mu}{\hat{ɛ} = {\frac{1}{d}{\sum\limits_{i = 1}^{d}\;{\hat{ɛ}}_{i}}}}} & (8) \\{\mspace{70mu}{{{- \infty} \leq \hat{\lambda}},{\hat{\lambda}}_{1},\ldots\;,{{\hat{\lambda}}_{d} \leq \infty}}} & (9) \\{\mspace{76mu}{\hat{\delta} \geq 0}} & (10) \\{\mspace{76mu}{{- {filt}} \leq \hat{\epsilon} \leq {filt}}} & (11) \\{\mspace{76mu}{{- \infty} \leq \hat{\lambda} \leq \infty}} & (12) \\{\mspace{76mu}{{{{beta\_ th}\_ 1} \leq {\hat{l}}_{0}},\ldots\;,{{\hat{l}}_{n - 1} \leq {{beta\_ th}{\_ u}}}}} & (13)\end{matrix}$

wherein beta_th_1 is the lower-limit on calibration break-point slopeand beta_th_u=upper-limit on calibration break-point slope, wherein filtand −filt are the limits on additive delivery correction term, whereinNd is the number of delivery points within the window w, and whereinN_(s) is the number of data-points, sales are reported within the givenwindow-size. Solving the set of linear equations using the optimizers,the model can evaluate {circumflex over (δ)}S_(i), which is thecorrective term associated with the error/drift in sales due tometer-drift in nozzles. Accordingly, the model can provide for anestimate of meter drift that is associated with one or more flow metersof fuel dispensers in fluid communication with the fuel storagefacility.

At 130, the estimate of meter drift can be provided. In someimplementations, the estimate of meter drift can be provided to a serverthat can generate a notification indicative of the estimate of meterdrift and provide the notification to an end terminal for furtherprocessing and/or display. In some implementations, the server can be aremote server located at a location that is different from that of thefuel storage facility and/or the fuel dispenser. In someimplementations, the server can be located at the same location as thefuel storage facility and/or the fuel dispenser (e.g., the fuelingstation). In some implementations, the end terminal can be located at alocation that is different from that of the fuel storage facility and/orthe fuel dispenser. In some implementations, the end terminal can belocated at the same location as the fuel storage facility and/or thefuel dispenser (e.g., the fueling station). In some implementations, thenotification can be a visual alert that is displayed on a display inoperable communication with any of the aforementioned servers and endterminals to thereby indicate the possible existence of the meter drift.In some implementations, the estimate of meter drift can be presented ingraphical form to an end user, via one or more of the aforementioneddisplays, by providing graphical determinations of errors associatedwith the sale of fuel from the fuel storage facility (via the fueldispensers) and that are indicative of meter drift in the flow meters ofthe fuel dispensers.

In some implementations, the estimate of meter drift can be provided toa data processor for further processing. The data processor can use theestimate of meter drift to determine a meter drift loss quantityprediction that characterizes an estimated amount of loss associatedwith meter drift if the meter drift is not immediately corrected.

In some implementations, the current subject matter can be configured tobe implemented in a system 300, as shown in FIG. 2. The system 300 caninclude one or more of a processor 310, a memory 320, a storage device330, and an input/output device 340. Each of the components 310, 320,330 and 340 can be interconnected using a system bus 350. The processor310 can be configured to process instructions for execution within thesystem 100. In some implementations, the processor 310 can be asingle-threaded processor. In alternate implementations, the processor310 can be a multi-threaded processor. The processor 310 can be furtherconfigured to process instructions stored in the memory 320 or on thestorage device 330, including receiving or sending information throughthe input/output device 340. The memory 320 can store information withinthe system 300. In some implementations, the memory 320 can be acomputer-readable medium. In alternate implementations, the memory 320can be a volatile memory unit. In yet some implementations, the memory320 can be a non-volatile memory unit. The storage device 330 can becapable of providing mass storage for the system 100. In someimplementations, the storage device 330 can be a computer-readablemedium. In alternate implementations, the storage device 330 can be afloppy disk device, a hard disk device, an optical disk device, a tapedevice, non-volatile solid state memory, or any other type of storagedevice. The input/output device 340 can be configured to provideinput/output operations for the system 300. In some implementations, theinput/output device 340 can include a keyboard and/or pointing device.In alternate implementations, the input/output device 340 can include adisplay unit for displaying graphical user interfaces. In someimplementations, the system 300 can be in operable communication withone or more components of a fueling station 400, as shown in FIG. 3. Thefueling station 400 can include a fuel storage facility 410, which mayinclude one or more fuel tanks 420 disposed in the ground at the fuelingstation 400 that are configured to hold fuel at the fueling station 400.The one or more fuel tanks 420 of the fuel storage facility 410 can bein operable communication with one or more sensors 430 that are locatedproximate the fuel storage facility 410 and configured to acquire datacharacterizing the fuel stored in the one or more fuel tanks 420, theone or more fuel tanks 420, and the fuel storage facility 410. The oneor more sensors 430 can also be in operable communication with thesystem 300 such that the system 300 can receive the acquired data foruse in determining the estimate of meter drift.

The one or more fuel tanks 420 of the fuel storage facility 410 can alsobe in fluid and operable communication with a fuel dispenser 440, whichcan dispense the fuel contained in the one or more fuel tanks to acustomer. The fuel dispenser can include a flow meter 450 that isconfigured to determine a volumetric rate of fuel provided by one ormore nozzles of the fuel dispenser to a customer during a fueldispensing transaction and to provide the volumetric rate of fuel to aprocessor of the fuel dispenser, which can use the volumetric rate offuel to determine an amount of fuel dispensed by the one or more nozzlesof the fuel dispenser. The fuel dispenser 440 can be in operablecommunication with the system 300 such that the system 300 can receivedata from the fuel dispenser 440 characterizing the amount of fuelprovided to the customer from the one or more nozzles during the sale offuel to the customer, which is based on the volumetric rate of fuelprovided by the flow meter 450. This data can be used by the system 300to determine the estimate of meter drift in accordance with the methodsand techniques described elsewhere herein.

FIG. 4 is a flow chart that demonstrates an exemplary process 500 fordetermining an estimate of meter drift and a prediction of lossassociated with meter drift that incorporates some implementations ofthe current subject matter as described herein. After initiating theprocess 500 is initiated at step 502, the sensor can, at step 504,acquire the data characterizing the fuel storage facility from the fuelstorage facility. At step 506, the data characterizing the fuel storagefacility can be received by a processor. The processor can execute apredictive model as described herein, at step 508, to determine varioussources of error/loss in the wetstock (e.g., fuel) contained within thefuel storage facility. At step 510, the processor can assess whether anyof the determined sources of error/loss can be attributed to meterdrift. If one or more of the determined sources of error/loss cannot beattributed to meter drift, the process pauses for a predefined period oftime (e.g., a day) at step 512 before restarting again at step 502.

If one or more of the determined sources of error can be attributed tometer drift, the processor can then determine an estimate of meter driftat step 514 and thereby assess whether the calibration of a flow meterin fluid and operable communication with the fuel storage facility hasdeviated from a predetermined calibration parameter for the flow meter.The processor can then, at step 516, determine a notification thatincludes the determined estimate of meter drift, and the processor can,at step 518, provide the notification for further use/analysis (e.g.,display the notification on a fuel dispenser attendant terminal, sendthe notification to a server configured to analyze the operations of thefuel dispenser, the fueling station at which the fuel dispenser islocated, and/or the fuel storage facility, and the like.). In addition,the processor can determine a prediction of loss over time due to meterdrift based on the determined estimate of meter drift at step 520, and,at step 522, provide the prediction for further use/analysis (e.g.,display the prediction on a fuel dispenser attendant terminal, send theprediction to the server configured to analyze the operations of thefuel dispenser, the fueling station at which the fuel dispenser islocated, and/or the fuel storage facility, and the like.). When steps518 and 522 are completed, the process can then pause for a predefinedperiod of time (e.g., a day) at step 512 before restarting again at step402.

The estimate of meter drift determined by process 500 and otherprocesses/techniques described herein can be a historically-derivedestimate. As such, the predictive model can be executed on a repetitivebasis (e.g., a real-time or daily basis) using data acquired by thesensor on a repetitive basis (e.g., a real-time or daily basis). Assuch, by tracking the losses identified via the repetitive (e.g., daily)collection of fuel storage facility data and execution of the predictivemodel, the process 500 can provide an assessment of whether thecalibration of the flow meter is changing over time and thereby causingmeter drift. In addition, the prediction of loss over time due to meterdrift can be determined on a going-forward basis based on changes in thecalibration of the flow meter over time as determined by execution ofthe predictive model and by the determination of the estimate of meterdrift.

It should be noted that the steps shown in FIGS. 1 and 4 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein. Even further, the illustrated steps maybe modified in any suitable manner in accordance with the scope of thepresent claims.

Accordingly, the system as discussed herein can combine all known alertsand data points, site equipment, and infrastructure details into a modelto provide a user with the estimate of meter drift and to quantifylosses associated with meter drift. By applying artificial intelligenceand machine learning techniques to provide model and parameterrecommendations, the detection of flow meter calibration drift can beperformed more efficiently, thereby saving costs and improving safetyand regulatory compliance.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

One skilled in the art will appreciate further features and advantagesof the invention based on the above-described embodiments. Accordingly,the invention is not to be limited by what has been particularly shownand described, except as indicated by the appended claims. Allpublications and references cited herein are expressly incorporatedherein by reference in their entirety.

What is claimed is:
 1. A method comprising: receiving, from a sensor inoperable communication with a fuel storage facility, data characterizingthe fuel storage facility; determining, based on the received data, anestimate of meter drift of a flow meter of a fuel dispenser in fluidcommunication with the fuel storage facility, the determining furtherbased on at least one predictive model that predicts whether acalibration parameter characterizing a calibration of the flow meter hasdeviated from a predetermined flow meter calibration parameter; andproviding the estimate of meter drift.
 2. The method of claim 1, whereinthe at least one predictive model includes a predetermined calibrationparameter for the fuel storage facility, a physics model for the fuelstorage facility, and an error model indicative of at least one degreeof error in the data.
 3. The method of claim 2, further comprising:determining a meter drift loss quantity prediction for the fuel storagefacility, the determining of the meter drift loss quantity predictionbased on the received data, the predetermined calibration parameter forthe fuel storage facility, the physics model, and an optimization of theerror model.
 4. The method of claim 1, wherein the sensor includes oneor more of a dipstick, an automated tank gauge, a fuel leak detectionsensor, a magnetostrictive probe, a point of sale device, a forecourtcontroller, a back office system, and/or a fuel dispenser.
 5. The methodof claim 1, wherein the data characterizing the fuel storage facilityincludes one or more of an indication of leakage and/or a rate ofleakage per unit time of fuel from the fuel storage facility to thesurrounding environment, environmental parameters of the fuel storagefacility, an amount of fuel added to the fuel storage facility as aresult of a delivery of fuel from a fuel supplier to the fuel storagefacility, and/or an amount of fuel removed from the fuel storagefacility as a result of a sale of fuel to a customer.
 6. The method ofclaim 1, wherein the estimate of meter drift is provided to a graphicaluser interface of a display communicatively coupled to the server, thegraphical user interface configured to present a visual characterizationof the meter drift loss quantity prediction on the display.
 7. Themethod of claim 1, wherein the estimate of meter drift is provided to agraphical user interface of a display communicatively coupled to theserver, the graphical user interface configured to present a visualcharacterization of the estimate of meter drift on the display.
 8. Themethod of claim 1, further comprising determining the estimate of meterdrift at a repeatable time interval.
 9. The method of claim 1, whereinthe physics model is a fluid balance model.
 10. The method of claim 1,wherein the determining is further based on mathematical programing andincludes maximizing or minimizing a function characterized by thephysics model and by at least varying input values of the function, theinput valves characterizing the received data, and computing an outputvalue of the function, the output value characterizing the estimate ofmeter drift.
 11. A system comprising: at least one data processor; andmemory storing instructions configured to cause the at least one dataprocessor to perform operations comprising: receiving, from a sensor inoperable communication with a fuel storage facility, data characterizingthe fuel storage facility; determining, based on the received data, anestimate of meter drift of a flow meter of a fuel dispenser in fluidcommunication with the fuel storage facility, the determining furtherbased on at least one predictive model that predicts whether acalibration parameter characterizing a calibration of the flow meter hasdeviated from a predetermined flow meter calibration parameter; andproviding the estimate of meter drift.
 12. The system of claim 11,wherein the at least one predictive model includes a predeterminedcalibration parameter for the fuel storage facility, a physics model forthe fuel storage facility, and an error model indicative of at least onedegree of error in the data.
 13. The system of claim 12, wherein theoperations further comprise: determining a meter drift loss quantityprediction for the fuel storage facility, the determining of the meterdrift loss quantity prediction based on the received data, thepredetermined calibration parameter for the fuel storage facility, thephysics model, and an optimization of the error model.
 14. The system ofclaim 11, wherein the sensor includes one or more of a dipstick, anautomated tank gauge, a fuel leak detection sensor, a magnetostrictiveprobe, a point of sale device, a forecourt controller, a back officesystem, and/or a fuel dispenser.
 15. The system of claim 11, wherein thedata characterizing the fuel storage facility includes one or more of anindication of leakage and/or a rate of leakage per unit time of fuelfrom the fuel storage facility to the surrounding environment,environmental parameters of the fuel storage facility, an amount of fueladded to the fuel storage facility as a result of a delivery of fuelfrom a fuel supplier to the fuel storage facility, and/or an amount offuel removed from the fuel storage facility as a result of a sale offuel to a customer.
 16. The system of claim 11, wherein the estimate ofmeter drift is provided to a graphical user interface of a displaycommunicatively coupled to the server, the graphical user interfaceconfigured to present a visual characterization of the meter drift lossquantity prediction on the display.
 17. The system of claim 11, whereinthe physics model is a fluid balance model.
 18. The system of claim 11,wherein the determining is further based on mathematical programing andincludes maximizing or minimizing a function characterized by thephysics model and by at least varying input values of the function, theinput valves characterizing the received data, and computing an outputvalue of the function, the output value characterizing the estimate ofmeter drift.
 19. The system of claim 11, wherein the operations furthercomprise determining the estimate of meter drift at a repeatable timeinterval.
 20. A non-transitory computer program product storinginstructions which, when executed by at least one data processor formingpart of at least one computing system, cause the at least one dataprocessor to implement operations comprising: receiving, from a sensorin operable communication with a fuel storage facility, datacharacterizing the fuel storage facility; determining, based on thereceived data, an estimate of meter drift of a flow meter of a fueldispenser in fluid communication with the fuel storage facility, thedetermining further based on at least one predictive model that predictswhether a calibration parameter characterizing a calibration of the flowmeter has deviated from a predetermined flow meter calibrationparameter; and providing the estimate of meter drift.